This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. The goal of the proposed project is to obtain anisotropic elastic properties of soft tissues from shear wave displacement measurements. This will be achieved by first simulating the forward problem with the finite element method i.e. shear wave propagation in an elastic material. Second, an inverse method will be setup based on measured displacements from elastography experiments and predicted displacements from the forward problem. The inverse finite element model will be solved by minimizing an error function based on measured and predicted displacements by correcting for the anisotropic material parameters. Solving the inverse finite element model is a computationally intensive task due to (a) large number of degrees of freedom (usually >10,000) in the finite element model (b) the large number of unknown material parameters that need to be solved for in the inverse problem. The inverse solution will require multiprocessor hardware and parallel algorithms to enhance the speed of solution of the inverse problem. We propose to implement macro-level parallelization of the inverse algorithm through subzone based domain decomposition to reduce the number of degrees of freedom treated at one time on each processor to tractable levels. With suitable multiprocessor hardware the proposed algorithms potentially reduce the computational time without sacrificing parameter accuracy.